Additive manufacturing-coupled digital twin ecosystem based on a surrogate model of measurement

ABSTRACT

There are provided methods and systems for making or repairing a specified part. For example, there is provided a method for creating an optimized manufacturing process to make or repair the specified part. The method includes receiving data from a plurality of sources, the data including as-designed, as-manufactured, as-simulated, and as-tested data relative to one or more parts similar to the specified part. The method includes updating, in real time, a surrogate model corresponding with a physics-based model of the specified part, wherein the surrogate model forms a digital twin of the specified part. The method includes further updating the surrogate model with a model of manufactured variance associated with at least one of inspection and in-operation data of a similar part. The method includes executing, based on the digital twin, the optimized manufacturing process to either repair or make the specified part.

I. CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims benefit to U.S. Provisional Patent ApplicationNos. 62/862,012 and 62/862,016 filed on Jun. 14, 2019. The disclosuresof both prior applications are incorporated herein in their entirety byreference.

II. BACKGROUND

In industrial applications the production of a component often includesconsidering the manufacturing process at the design stage. In suchcases, the design and the manufacturing processes are closely related,meaning that design decisions may be influenced by manufacturingconstraints or that manufacturing choices may result directly fromaspects of the design. Moreover, operational characteristics may beinfluenced by the manufacturing process' capabilities. For instance, intypical industrial manufacturing processes, parts are produced accordingto pre-determined tolerances because the as-manufactured parts that aredeployed in the field may differ from their design specifications (i.e.,from the as-designed parts) due to variations inherent to themanufacturing processes.

With the advent of additive manufacturing technology, another layer ofcomplexity is introduced in the above-notedmanufacturing/design/operation ecosystem because of the inherent aspectsof additive processes. For example, the additive process may use layersof materials by addition to form the component and pre/post treatmentsteps such as heating and curing of the layers. Optimizing andvalidating the additive process requires quantifying and validating thevariances in the manufactured components by destructive testing thatproduces significant quantities of scrap material dependent of thenumber of tolerances tested.

Destructive testing alone may validate that a manufactured componentmeets a specific design tolerance but not consider how the influences ofmultiple within tolerance variances aggregately affect performance ofthe component in operation or replicate the range of operating regimethat components are exposed to in operation and therefore quantify thefitness of components manufactured by a process for operation. A furtherrisk is that manufactured components with a useful and serviceable lifeare scrapped as the influence of variances occurring during themanufacturing cycle and the fitness of a component for operation is notquantifiable.

III. SUMMARY

The embodiments featured herein help solve or mitigate the above-notedissues as well as other issues known in the art. The embodimentsfeatured herein integrate operational characteristics, as they aremeasured and analyzed during a component's life cycle, with design andmanufacturing, including specific aspects of additive manufacturingprocesses, to create models capable of mitigating performance andmanufacturing variances.

For example, the embodiments provide the ability to link as-built,as-manufactured/assembled, as-designed and as-simulated, as-tested,as-operated and as-serviced components directly through a unique digitalintegrated process. This digital integrated process includes specificaspects of additive manufacturing processes used at any point during acomponent's life cycle. In the embodiments featured herein, any hardwarecomponent has the capability to reference to its design goal and derivemultiple analysis outcomes based on its hardware specifications andoperational data. The novel process also provides abstraction of datatypes from multiple analyses to form an integrated digital twin ofhardware components. Furthermore, the novel process provides a frameworkto increase fidelity and accuracy of a system level digital twin byaggregating sub-system component level digital twin predictions.

The embodiments featured herein provide a technological infrastructurethat yield automated, quantitative, and qualitative assessments of thevariability in additive manufacturing processes during the useful lifeof a part. Thus, in their implementation, the embodiments purposefullyand effectively allow the optimization of a manufacture or repairprocess to make or repair components to a useful lifetime specified bythe application's constraints while optimizing the quantity of materialneeded and destructive testing required for producing or repairing thepart using one or more additive manufacturing processes. For example,and not by limitation, in the case of a component requiring a coating,an embodiment as set forth herein can provide a quantitative assessmentof the amount of coating material needed to be added onto the componentin order to match the performance of the component during repair ormanufacturing; the amount of material identified can be optimizedagainst cost constraints.

Additional features, modes of operations, advantages, and other aspectsof various embodiments are described below with reference to theaccompanying drawings. It is noted that the present disclosure is notlimited to the specific embodiments described herein. These embodimentsare presented for illustrative purposes only. Additional embodiments, ormodifications of the embodiments disclosed, will be readily apparent topersons skilled in the relevant art(s) based on the teachings provided.

IV. BRIEF DESCRIPTION OF THE DRAWINGS

Illustrative embodiments may take form in various components andarrangements of components. Illustrative embodiments are shown in theaccompanying drawings, throughout which like reference numerals mayindicate corresponding or similar parts in the various drawings. Thedrawings are only for purposes of illustrating the embodiments and arenot to be construed as limiting the disclosure. Given the followingenabling description of the drawings, the novel aspects of the presentdisclosure should become evident to a person of ordinary skill in therelevant art(s).

FIG. 1 illustrates a process according to an embodiment.

FIG. 2 illustrates a digital twin ecosystem according to an embodiment.

FIG. 3 illustrates an exemplary process according to an aspect of anembodiment.

FIG. 4 illustrates an exemplary process according to an aspect of anembodiment.

FIG. 5 illustrates an exemplary process according to an aspect of anembodiment.

FIG. 6 illustrates an exemplary process according to an aspect of anembodiment.

FIG. 7 illustrates an exemplary process according to an aspect of anembodiment.

FIG. 8 illustrates an exemplary process according to an aspect of anembodiment.

FIG. 9 illustrates an exemplary system configured to execute one or moreaspects of the exemplary processes presented herein.

V. DETAILED DESCRIPTION

While the illustrative embodiments are described herein for particularapplications, it should be understood that the present disclosure is notlimited thereto. Those skilled in the art and with access to theteachings provided herein will recognize additional applications,modifications, and embodiments within the scope thereof and additionalfields in which the present disclosure would be of significant utility.

The embodiments featured herein have several advantages. For example,they can allow one to make accurate assessments on the quality of newmake parts relative to their design intent. They provide the ability tomix and match different manufactured components in an engine assembly toachieve a desired integrated engine performance. Furthermore, theyimprove time-on-wing assessments of every part and sub-assembly based onmanufacturing variations, operational conditions, and as-servicedconditions. The embodiments help leverage the sub-system assemblyperformance using high fidelity design knowledge, and they improveprediction accuracy as required. Furthermore, they enable feedback loopsthat help improve subsequent designs.

FIG. 1 illustrates an exemplary process 100 in accordance with anexemplary embodiment. The process 100 may be an example processassociated with the lifecycle of a part and/or a general manufacturingcycle. While the process 100 is described in the context of air plane orjet engine parts, it may extend to the manufacture or in general to thelifecycle of any manufactured component. The process 100 includes amodule 102 that is a product environment spectrum. In other words, themodule 102 can be a database that stores information about instances ofthe same product as they are used in the field.

For example, the module 102 may include information about thereliability or failure of a plurality of turbine blades as they arecommissioned in a fleet of engines (i.e., in two or more engines). Themodule 102 may be configured to organize, or present upon request from adevice communicatively coupled thereto, a product environment spectrumwhich sorts all of the products of interest in a predetermined order.

For example, the products may be sorted based on their robustness. Inone use case, the products may be sorted from more robust (102 a) toleast robust (102 n). Generally, one or more performance criteria may beused to sort these products according to the aforementioned spectrum. Inthe case of a turbine blade, the products may be sorted according totheir thermal robustness performance, which may be measured using one ormore field inspection methods.

The product environment spectrum may be driven by constraints fromcustomers, which may be collected and functionalized (i.e., put in theform of computer instructions) in the module 104. In other words, therobustness criteria may be dictated by application-specific parametersderived from customers. Similarly, the product environment spectrum maybe driven by commercial constraints, which may be functionalized in themodule 106. These constraints (for both the modules 104 and 106) may beupdated as the manufacturing process is updated in view of the varioussources of information, as shall be further described below.

The customer constraints of the module 104 may also drive themanufacturing functions of the module 108, which in turn drive theengineering decisions, as functionalized in the module 112. Once theengineering decisions are functionalized, they may be used to establisha digital thread that is configured for design. The digital designthread may also be updated from the constraints of the customers (module104). This thread thus forms a digital twin which can be formed frommultiple data sources representing multiple use case. In other words,the digital twin integrates multiple use cases to ensure thatmanufactured parts are produced according to specific performance datarather than merely producing parts according to predetermineddimensional constraints, as is done in typical manufacturing processes.

Therefore, the digital twin allows for engineering re-design based onfielded part performance. As such, the digital twin allows theoptimization of a given manufacturing process in order to differentiatequality of as-manufactured parts to drive targeted performance andbusiness outcomes.

Generally, the digital design twin may be constructed from a pluralityof sources that include new make manufacturing data from the engineeringmodel, a network and an already existing manufacturing model of the part(module 108). Data streams from the network, may include, for exampleand not by limitation, borescope inspection data from field inspections(either partial or full, or in some implementations, functional ordimensional inspections), on-wing probes that measure data from anengine during flight. Furthermore, generally, the digital twin of acomponent may include at least one of as-manufactured data, as-testeddata, as-designed and as-simulated, as-operated data, and as-serviceddata of the component. Furthermore, the digital twin of the componentmay be based on operational data or nominal operating conditions of thecomponent.

The process 100 allows data to be collected continuously. Specifically,the digital design thread is continuously updated to provide a modelreflecting actual conditions. This is done with the explicit feedbackloops of the process 100, which ensure that new designs can bemanufactured based the wide variety of sources of information mentionedabove. As such, the process 100 provides the ability to better predictthe durability of a part, as any manufactured part would have beenmanufactured based on conditions reflecting design, usage, servicing,etc.

In sum, the process 100 integrates and automates the various aspect ofthe lifecycle of the part to provide an optimized manufacturing processat an enterprise level. The process 100 further includes a scoreinspection module, which may be updated with field inspection analytics,in order to further augment the engineering model. The process 100 canbe further understood in the context of FIG. 2, which depicts thedigital twin ecosystem 200 featuring exemplary relationships between theas-designed, as manufactured, as-tested, as-serviced, and as-operatedaspects of a specified part during its life cycle. The digital twinecosystem 200 includes aspects which accounts for additive manufacturingprocess variance, as shall be described in further detail below.

FIG. 3 illustrates an exemplary process including an as-designed,as-manufactured, as-tested, as-operated, as-inspected, and as-servicedadditive manufacturing-coupled digital twin ecosystem. FIG. 3 depicts anoperating spectrum and an environmental spectrum. These spectra form the‘operational regime’, which denotes a gradation in operation (e.g. fromlight to hard of the specified part) as well as an environment in whichthe specified part operates (e.g., from benign to harsh). An operationalregime is a factor of the environment and the operation. The performanceof a component is thus quantifiable and comparable with similarcomponents within similar operating regimes. The performance of a partis an indicator of remaining useful life within a range of operatingregimes.

The process performance is a spectrum from ‘As process designed’performance and tolerance to out of tolerance. Thus, outside of the ‘asprocess designed’ performance may imply more or less processing ormaterial applied to a component, e.g. flow rate and nozzle indicatingminimum thickness of coating applied to component. In the embodiment, ofFIG. 3, a model of manufactured variance to inspected measurements andperformance of parts in operation may be used to in the context of theprocess 100 to create a production model of component performance.

FIG. 4 illustrates an exemplary process including an as-designed,as-manufactured, as-tested, as-operated and as-serviced additivemanufacturing-coupled digital twin ecosystem. In FIG. 4, when a shift isobserved in a manufacturing process by an exemplary system (see FIG. 9)in ‘Process X’, e.g. a change in flow rate from a nozzle that may implythat less thermal barrier has been applied, the exemplary system may beconfigured to determine that a similar shift was observed duringmanufacture of parts with serial number 1 . . . 3 in ‘Process X’.Furthermore, the exemplary system may be configured to predict, based onthe operational and environmental regimes experienced X1,2,3 and theperformance, X, of those components through their operating life,‘Cycles’, e.g. thermal performance, the range of useful life based onexpected in production performance and performance degradation, X, ofmanufactured component through its operating life, ‘Cycles’.

The exemplary system may then be configured to suggest a suitableoperating regime, X1,2,3, for that component to achieve bestin-production performance. The in-service performance and operationalregime, X, may be determined by operational experience of components inproduction and/or be simulation e.g. using computational fluid dynamics.This reduces scrappage and warranty claims as the exemplary system doesnot base scrappage on meeting an ‘as designed’ or ‘as process designed’specification and warranty or price the component as per it's expecteduseful life or for a particular operating regime.

FIG. 5 illustrates an exemplary process including an as-designed,as-manufactured, as-tested, as-operated and as-serviced additivemanufacturing-coupled digital twin ecosystem. In FIG. 5, the exemplarysystem may be configured to determine a shift in the manufacturingprocess, ‘process X’. This shift may be, for example and not bylimitation, a flow rate from a nozzle that may imply that less thermalbarrier has been applied, which would result in a range of in productionperformance. For instance, such range may be a variation in thermalperformance and performance degradation, X, depending on operatingregime, X1,2,3. Given this information, the exemplary system canmanufacture a component with a shift in ‘Process X’, to achieve theperformance of Y that degrades according to the progression of X whenoperated within the operating regime X2Y.

For example, this component may be manufactured for operation where theoperating regime X2Y denotes light operation in a benign environment.The in-service performance and operational regime, X, may be determinedby operational experience of components in production and/or simulatione.g. using computational fluid dynamics. The benefit of manufacturing toY being a reduction of the process or material used to achieve thedesired performance Y.

FIGS. 6 and 7 illustrate an exemplary process including an as-designed,as-manufactured, as-tested, as-operated and as-serviced additivemanufacturing-coupled digital twin ecosystem. In FIG. 7, the exemplarysystem may be configured to determine a shift in the manufacturingprocess, ‘process X’. This shift may be, for example and not bylimitation, a flow rate from a nozzle that may imply that less thermalbarrier has been applied, which would result in a range of in productionperformance. For instance, such range may be a variation in thermalperformance and performance degradation, X, depending on operatingregime, X1,2,3.

Given this information, the exemplary system can infer an in-productionperformance, e.g. thermal performance, in the range of X. A shift in‘Process Y’, such as a post treatment step like a heat treatment of thethermal barrier, may also result independently in a performance, Y,within acceptable range. However, the combined influence of X+Y may begreater than the influence of X and Y independently where the resultingperformance of X+Y=Z in the range of X1Y1, X2Y2, X3Y3. Thus, creating amodel of a manufactured part that quantifies quality as a factor ofpredicted in production performance, e.g. thermal performance, has theadvantage of allowing the prediction of the impact of multiple processinfluencers, X & Y, on end component performance. Further, thein-service performance and operational regime, X & Y, may be determinedby operational experience of components in production and/or simulatione.g. using computational fluid dynamics.

As such, the exemplary system has an advantage over destructive testingmethods where destructive tests do not independently correlate theaggregate influence of multiple process influencers and cannotreasonably replicate the range of operating regime as can be observed inservice and/or simulated. Providing an alternative or parallelqualification process to destructive testing has the benefits ofimproving safety, reducing warranty claim and reducing scrappage.

FIG. 8 illustrates an exemplary process including an as-designed,as-manufactured, as-tested, as-operated and as-serviced additivemanufacturing-coupled digital twin ecosystem. In FIG. 8, if we know orcan predict through operational experience and/or simulation of themanufactured components the in service performance, X, over a range ofoperating regimes, X1,X2,X3, of components manufactured to aspecification, e.g. D=‘As Designed’, and how they degrade based onmeasurement over their operational life, ‘Cycles’, we can quantify theprocess and/or material required, ‘Process X’, to restore a component tothe measurement at specification, D, by quantifying the regression fromthe expected measurement of X at a service life, ‘Cycles’, and operatingregime, X1,X2,X3 to the specification, D.

To restore component Y to D, ‘As designed’, where Y has an operationalperformance of X1 and has been operated under an operational regime ofX1Y and 1000 cycles we need to calculate the regression of X1 at 1000cycles, materials and/or process required ‘Process X’, to restore to D.This can be used to qualify a repair for an individual component, Y, ora repair process for components performing in the range of X with thebenefit of being used as a specification of a repair or a qualificationof a repair process where the process may also be validated by physicaltesting; where reducing destructive testing requirement reducesscrappage. We also have the benefit of being able to predict the costand material demand of a repair process while components are in servicethroughout their operational life by the progression of X to optimizerepair cost vs in service performance vs optimum service interval.

FIG. 9 depicts a system 1000 that executes the various operationsdescribed above in the context of the exemplary digital twin ecosystemdescribed in the processes described in regards to FIGS. 1-8. The system1000 includes an application-specific processor 1014 configured toperform tasks specific to optimizing a manufacturing process accordingto the 100. The processor 1014 has a specific structure imparted byinstructions stored in a memory 1002 and/or by instructions 1018 thatcan be fetched by the processor 1014 from a storage 1020. The storage1020 may be co-located with the processor 1014, or it may be locatedelsewhere and be communicatively coupled to the processor 1014 via acommunication interface 1016, for example.

The system 1000 can be a stand-alone programmable system, or it can be aprogrammable module located in a much larger system. For example, thesystem 1000 be part of a distributed system configured to handle thevarious modules of the process 100 described above. The processor 1014may include one or more hardware and/or software components configuredto fetch, decode, execute, store, analyze, distribute, evaluate, and/orcategorize information.

The processor 1014 can include an input/output module (I/O module 1012)that can be configured to ingest data pertaining to single assets orfleets of assets. The processor 1014 may include one or more processingdevices or cores (not shown). In some embodiments, the processor 1014may be a plurality of processors, each having either one or more cores.The processor 1014 can be configured to execute instructions fetchedfrom the memory 1002, i.e. from one of memory block 1004, memory block1006, memory block 1008, and memory block 1010.

Furthermore, without loss of generality, the storage 1020 and/or thememory 1002 may include a volatile or non-volatile, magnetic,semiconductor, tape, optical, removable, non-removable, read-only,random-access, or any type of non-transitory computer-readable computermedium. The storage 1020 may be configured to log data processed,recorded, or collected during the operation of the processor 1014. Thedata may be time-stamped, location-stamped, cataloged, indexed, ororganized in a variety of ways consistent with data storage practice.The storage 1020 and/or the memory 1002 may include programs and/orother information that may be used by the processor 1014 to performtasks consistent with those described herein.

For example, the processor 1014 may be configured by instructions fromthe memory block 1006, the memory block 1008, and the memory block 1010,to perform real-time updates of a model for a part based on a variety ofinput sources (e.g. a network and/or a field data module 108). Theprocessor 1014 may execute the aforementioned instructions from memoryblocks, 1006, 1008, and 1010, and output a twin digital model that isbased on data from the wide variety of sources described above. Statedgenerally, from the continuous updates, the processor 1014 maycontinuously alter the strategy deployment module 110 that includes themodel for the part based on the prognostic deployment or degradationmodels described in the context of FIG. 2-9.

The embodiments provide the capability to improve time on wingassessments of every part and its sub-assembly based on manufacturingvariations, operational conditions and as-serviced data. Furthermore,the embodiments help leverage the sub-system assembly performance usinghigh fidelity design knowledge and improve prediction accuracy asrequired, and they enable feedback loop that help improve subsequentdesigns.

Those skilled in the relevant art(s) will appreciate that variousadaptations and modifications of the embodiments described above can beconfigured without departing from the scope and spirit of thedisclosure. Therefore, it is to be understood that, within the scope ofthe appended claims, the disclosure may be practiced other than asspecifically described herein.

What is claimed is:
 1. A method for making or repairing a specifiedpart, the method including: creating an optimized manufacturing processto make or repair the specified part, the creating including: receivingdata from a plurality of sources, the data including as-designed,as-manufactured, as-simulated, and as-tested data relative to one ormore parts similar to the specified part; updating, in real time, asurrogate model corresponding with a physics-based model of thespecified part, wherein the surrogate model forms a digital twin of thespecified part; further updating the surrogate model with a model ofmanufactured variance associated with at least one of inspection andin-operation data of a similar part; executing, based on the digitaltwin, the optimized manufacturing process to either repair or make thespecified part.
 2. The method as set forth in claim 1, wherein thesurrogate model further includes measurements recorded during inspectionof components that can be correlated with component operation andperformance of components in operation.
 3. The method as set forth inclaim 2, wherein the surrogate model is further configured to correlatewith measurement variance monitored during an additive/reductivemanufacture/repair step to create a model of measurement varianceobserved during additive/reductive manufacture/repair; and wherein theadditive/reductive manufacturing/repair step include multipleadditive/reductive processes and post treatments steps and machines. 4.The method as set forth in claim 3, wherein the surrogate model isfurther configured to correlate with expected performance of componentsin operation.
 5. The method as set forth in claim 4, further includingcalculating a useful life of the specified part based on a correlationwith the expected performance of components in operation.
 6. The methodas set forth in claim 1, further including using surrogate model tocompare measurements recorded during inspection of a component tooptimize an additive/reductive repair process required to returncomponent to desired performance.
 7. The method as set forth in claim 6,further including correlating the desired performance of the componentto an expected remining useful life.
 8. The method as set forth in claim7, wherein optimizing the additive/reductive repair process reducesmaterial utilization or additive/reductive processing by achieving thedesired component performance relative to an as designed performance. 9.The method as set forth in claim 1, wherein the surrogate model isfurther based on correlations between observations/measurement recordedduring inspection of the component and component operational data andcharacteristics of the performance of the component in operation togroup components according to observation/measurements and operation toperformance of component in operation.
 10. The method as set forth inclaim 9, further including using the model of observation/measurement toperformance of component in operation to create a prognostic model ofpredicted performance of components in operation based onobservation/measurement of component recorded during inspection ofcomponent at point of additive/reductive manufacture/repair.
 11. Themethod as set forth in claim 10, wherein the prognostic model ofpredicted performance of components in operation is based onobservation/measurement of component recorded during inspection ofcomponent at point of additive/reductive manufacture/repair.
 12. Themethod as set forth in claim 11, further including using the prognosticmodel to calculate a useful/remaining useful life of a component or setof components based on observation/measurement of components at a pointof additive/reductive manufacture/repair.
 13. A system configured toeither manufacture or repair a specified part, the system comprising: aprocessor; a memory including instructions that, when executed by theprocessor, cause the processor to perform operations including: creatingan optimized manufacturing process to make or repair the specified part,the creating including: receiving data from a plurality of sources, thedata including as-designed, as-manufactured, as-simulated, and as-testeddata relative to one or more parts similar to the specified part;updating, in real time, a surrogate model corresponding with aphysics-based model of the specified part, wherein the surrogate modelforms a digital twin of the specified part; further updating thesurrogate model with a model of manufactured variance associated with atleast one of inspection and in-operation data of a similar part;executing, based on the digital twin, the optimized manufacturingprocess to either repair or make the specified part.
 14. The system asset forth in claim 13, wherein the surrogate model further includesmeasurements recorded during inspection of components that can becorrelated with component operation and performance of components inoperation.
 15. The system as set forth in claim 14, wherein thesurrogate model is further configured to correlate with measurementvariance monitored during an additive/reductive manufacture/repair stepto create a model of measurement variance observed duringadditive/reductive manufacture/repair.
 16. The system as set forth inclaim 15, wherein the surrogate model is further configured to correlatewith expected performance of components in operation.
 17. The system asset forth in claim 16, wherein the operations further includecalculating a useful life of the specified part based on a correlationwith the expected performance of components in operation.
 18. The systemas set forth in claim 1, wherein the operations further include usingsurrogate model to compare measurements recorded during inspection of acomponent to optimize an additive/reductive repair process required toreturn component to desired performance.
 19. The system as set forth inclaim 18, wherein the operations further include correlating the desiredperformance of the component to an expected remining useful life. 20.The system as set forth in claim 19, wherein the operations furtherinclude optimizing the additive/reductive repair process reducesmaterial utilization or additive/reductive processing by achieving thedesired component performance relative to an as designed performance.21. The system as set forth in claim 13, wherein the surrogate model isfurther based on correlations between observations/measurement recordedduring inspection of the component and component operational data andcharacteristics of the performance of the component in operation togroup components according to observation/measurements and operation toperformance of component in operation.
 22. The system as set forth inclaim 21, wherein the operations further include using the model ofobservation/measurement to performance of component in operation tocreate a prognostic model of predicted performance of components inoperation based on observation/measurement of component recorded duringinspection of component at point of additive/reductivemanufacture/repair.
 23. The system as set forth in claim 22, wherein theprognostic model of predicted performance of components in operation isbased on observation/measurement of component recorded during inspectionof component at point of additive/reductive manufacture/repair.
 24. Themethod as set forth in claim 23, wherein the operations further includeusing the prognostic model to calculate a useful/remaining useful lifeof a component or set of components based on observation/measurement ofcomponents at a point of additive/reductive manufacture/repair.